MCP - Kestrel AI

The Kestrel MCP server exposes workflow management as tools that AI coding agents (Cursor, Claude Desktop, OpenAI Codex, etc.) can call directly. This lets you create, modify, and monitor workflows through conversational AI.

Prerequisites

Install the Kestrel CLI and authenticate:

brew install KestrelAI/tap/kestrel
kestrel auth kestrel_sk_...

The MCP server uses your CLI credentials stored at ~/.kestrel/config.json.

Setup

The MCP server runs as a subcommand of the CLI. Configure your AI tool to run kestrel mcp.

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "kestrel": {
      "command": "kestrel",
      "args": ["mcp"]
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "kestrel": {
      "command": "kestrel",
      "args": ["mcp"]
    }
  }
}

OpenAI Codex

Add via the Codex CLI:

codex mcp add kestrel -- kestrel mcp

Or add to ~/.codex/config.toml:

[mcp_servers.kestrel]
command = "kestrel"
args = ["mcp"]

If kestrel isn’t on your PATH, use the full path (e.g., /usr/local/bin/kestrel).

Available Tools

Workflow Management

Tool	Description
`list_workflows`	List all workflows, optionally filtered by status
`get_workflow`	Get workflow details including definition and trigger config
`create_workflow`	Create a new workflow from a definition
`update_workflow`	Update a workflow’s name, description, or definition
`delete_workflow`	Delete a workflow
`activate_workflow`	Activate a workflow
`pause_workflow`	Pause an active workflow
`duplicate_workflow`	Copy a workflow as a new draft

Workflow Generation

Tool	Description
`generate_workflow`	Generate a workflow from natural language
`request_workflow`	Submit a request to trigger a matching workflow. If nothing matches, returns `pending_confirmation` with a `request_id`
`confirm_workflow_request`	Resolve a `pending_confirmation` request: confirm to send it to the platform team, or dismiss it. The agent asks you before calling this
`list_suggested_workflows`	Get AI-suggested workflows

Execution Management

Tool	Description
`list_executions`	List executions for a workflow
`get_execution`	Get execution details including step results
`cancel_execution`	Cancel a running execution
`get_workflow_stats`	Aggregate execution statistics

Approval Management

Tool	Description
`list_pending_approvals`	List pending approval gates
`approve_step`	Approve a gate (with optional justification)
`reject_step`	Reject a gate

Request Management

Tool	Description
`list_unmatched_requests`	List unmatched workflow requests
`approve_workflow_request`	Approve a request
`reject_workflow_request`	Reject a request

Catalog and Integrations

Tool	Description
`get_catalog`	Available signal triggers and action blocks
`get_integrations_status`	Integration connection status

Integration Management

Tool	Description
`list_integrations`	List every integration with its type, required credential fields, and connection status
`connect_integration`	Connect a token or knowledge-source integration, or get the browser URL for OAuth integrations (GitHub, GitLab, Slack)
`test_integration`	Test a connected integration’s credentials
`disconnect_integration`	Disconnect a token integration or knowledge source

Kubernetes, AWS, and OCI use multi-step flows and must be connected with the CLI (kestrel integrations connect <name>) instead of MCP tools. For credential safety, prefer the CLI’s interactive hidden prompts for secrets rather than pasting tokens into an agent chat.

Example Conversations

Creating a workflow:

“Create a workflow that triggers when a PagerDuty alert fires, runs K8s RCA, and posts results to #incidents on Slack”

The AI agent calls generate_workflow, reviews the result, then create_workflow and activate_workflow. Checking executions:

“Show me the last 5 failed workflow executions and what went wrong”

The agent calls list_workflows, list_executions, then get_execution for failed ones. Approving a gate:

“Are there any pending approvals? Approve the production deployment one”

The agent calls list_pending_approvals, identifies the right one, and calls approve_step. Connecting an integration:

“Connect our PagerDuty account to Kestrel”

The agent calls list_integrations to find the required credential fields, asks you for the values, then calls connect_integration and verifies with test_integration. Requesting a change with no matching workflow:

“Request a Redis cluster for the cache layer”

The agent calls request_workflow. When no workflow matches, it gets back pending_confirmation and asks whether you want to send the request to your platform team; based on your answer, it calls confirm_workflow_request to file or dismiss it.

​Prerequisites

​Setup

​Cursor

​Claude Desktop

​OpenAI Codex

​Available Tools

​Workflow Management

​Workflow Generation

​Execution Management

​Approval Management

​Request Management

​Catalog and Integrations

​Integration Management

​Example Conversations

Prerequisites

Setup

Cursor

Claude Desktop

OpenAI Codex

Available Tools

Workflow Management

Workflow Generation

Execution Management

Approval Management

Request Management

Catalog and Integrations

Integration Management

Example Conversations